The task of spoken pass-phrase verification is to decide whether
a test utterance contains the same phrase as given enrollment
utterances. Beside other applications, pass-phrase verification
can complement an independent speaker verification subsystem
in text-dependent speaker verification. It can also be used for
liveness detection by verifying that the user is able to correctly
respond to a randomly prompted phrase. In this paper, we build
on our previous work on i-vector based text-dependent speaker
verification, where we have shown that i-vectors extracted using
phrase specific Hidden Markov Models (HMMs) or using Deep
Neural Network (DNN) based bottle-neck (BN) features help to
reject utterances with wrong pass-phrases. We apply the same
i-vector extraction techniques to the stand-alone task of speakerindependent
spoken pass-phrase classification and verification.
The experiments on RSR2015 and RedDots databases show that
very simple scoring techniques (e.g. cosine distance scoring)
applied to such i-vectors can provide results superior to those
previously published on the same data.